artificial intelligence - ntnuweb.ntnu.edu.tw/~tcchiang/ai/0_syllabus.pdf · 11 21 artificial...

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Artificial Intelligence, Spring, 2010

Artificial Intelligence

Instructor: Tsung-Che [email protected]

Department of Computer Science and Information EngineeringNational Taiwan Normal University

2Artificial Intelligence, Spring, 2010

Texts

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Grading Policy

In-class exercises & take-home assignments(65% ~ 85%) C/C++ programming skill is required. There will be at least 4 take-home assignments. Late submissions: 1~3 days with 10% penalty, 4~7 days

with 20% penalty The submission with 8-day or longer delay will not be

accepted.

Final exam (20 ~ 0%) Class participation (15%)


Syllabus Introduction to Intelligent Agents Search

Blind Search Informed Search Constraint Satisfaction Problem Adversarial Search

Logic Propositional Logic

Soft Computing Fuzzy Systems Artificial Neural Network Metaheuristics

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Agents

An agent is anything that can be viewed asperceiving its environment through sensors andacting upon that environment thorough actuators.

Artificial Intelligence: A Modern Approach, 2nd ed., Figure 2.9


Agents

What is a rational agent?Task environments (problems)

Definition Performance, Environment, Actuator, Sensor

Properties Observable, Deterministic, Static, etc.

Agent structureSimply reflexModel-basedGoal-basedUtility-based

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Agents

Utility-based agent



972 Homework 1

Best Cleaner

Thanks to Mr. Shi-Yau Yu for the interface.

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Problem Solving



Problem Solving

Example problems

8-queens

8-puzzle

Vacuum world

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Uninformed Search Strategies

Breadth-first searchUniform-cost searchDepth-first searchIterative deepening searchBidirectional search


972 Homework 2

Missionaries and Cannibals Problemhttp://www.learn4good.com/games/puzzle/boat.htm

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Informed Search Strategies

Greedy best-first searchA* searchMemory-bounded heuristic search

Artificial Intelligence: A Modern Approach, 2nd ed., Figure 4.3 & 4.7


Informed Search Strategies

Hill climbingOnline search

(972 Homework 3)

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Constraint Satisfaction Problem

Backtracking searchVariable & value ordering Constraint propagation Intelligent backtracking

Local searchProblem structure


972 Homework 4

http://www.agame.com/game/Connectors.html

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Adversarial Search



Adversarial Search

Minimax algorithmAlpha-beta pruningImperfect, real-time decisions

Evaluation function & cut-off test

Games including an element of chance

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Propositional Logic

ReasoningModus PonensAnd-Elimination Resolution Forward/Backward chaining Backtracking Local search


Fuzzy Systems(a) Boolean Logic. (b) Multi-valued Logic.

0 1 10 0.2 0.4 0.6 0.8 100 1 10

Degree of MembershipFuzzy

MarkJohnTom

Bob

Bill

11100

1.001.000.980.820.78

Peter

Steven

MikeDavid

ChrisCrisp

1

0000

0.240.150.060.010.00

Name Height, cm

205198181

167

155152

158

172179

208

150 210170 180 190 200160

Height, cmDegree ofMembership

Tall Men

150 210180 190 200

1.0

0.0

0.2

0.4

0.6

0.8

160

Degree ofMembership

170

1.0

0.0

0.2

0.4

0.6

0.8

Height, cm

Fuzzy Sets

Crisp Sets

Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4.1 & 4.2, Table 4.1

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Fuzzy Systems

150 210170 180 190 200160Height, cm

Degree ofMembership

Tall Men

150 210180 190 200

1.0

0.0

0.2

0.4

0.6

0.8

160

Degree ofMembership

Short Average ShortTall

170

1.0

0.0

0.2

0.4

0.6

0.8

Fuzzy Sets

Crisp Sets

Short Average

Tall

Tall

Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 4.3


Fuzzy Systems

Rule 1:IF Distance is ShortAND Health is GoodTHEN Action is Chasing

Rule 2:IF Distance is LongAND Health is GoodTHEN Action is Do Nothing

Rule 3:IF Distance is ShortAND Health is BadTHEN Action is Escaping

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Fuzzy Inference

MamdaniSugeno

Crisp Inputy1

0.1

0.71

0y1

B1 B2

Y

Crisp Input

0.20.5

1

0

A1 A2 A3

x1

x1 X(x = A1) = 0.5(x = A2) = 0.2

(y = B1) = 0.1(y = B2) = 0.7

A 31

0 X

1

y10 Y

0 .0

x 1 0

0 .1C 1

1

C 2

Z

1

0 X

0 .2

0

0 .2 C 11

C 2

Z

A 2

x1

R ule 3 :

A 11

0 X 0

1

Zx1

T H E N

C 1 C 2

1

y1

B 2

0 Y

0 .7

B 10 .1

C 3

C 3

C 30 .5 0 .5

O R(m a x )

A N D(m in )

O R T H E NR ule 1 :

A N D T H E NR ule 2 :

IF x is A 3 (0 .0 ) y is B 1 (0 .1 ) z is C 1 (0 .1 )

IF x is A 2 (0 .2 ) y is B 2 (0 .7 ) z is C 2 (0 .2 )

IF x is A 1 (0 .5 ) z is C 3 (0 .5 )

Artificial Intelligence: A Guide toIntelligent Systems, 2nd ed., Figure4.10


Artificial Neural Networks

Threshold

Inputs

x1

x2

Output

Y

HardLimiter

w2

w1

LinearCombiner

Soma Soma

Synapse

Synapse

Dendrites

Axon

Synapse

Dendrites

Axon

Input Layer Output Layer

Middle Layer

Inp

ut

Sig

na

ls

Ou

tpu

tS

ign

als

Biological Neural Network Artificial Neural NetworkSomaDendriteAxonSynapse

NeuronInputOutputWeight

Artificial Intelligence: A Guide to Intelligent Systems, 2nd ed., Figure 6.1, 6.2, 6.5

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Artificial Neural Networks

PerceptronBack-propagation networkHopfield networkBidirectional associative memorySelf-organizing map


Metaheuristics

Evolutionary computationAnt colony optimizationParticle swarm optimizationTabu search

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Genetic Algorithms

Stop?

Mating selection

Reproduction

Environmentalselection

Y

Initial Population

Final Population

Evaluation

NEvaluation

nextgeneration

generation 1

offspring

•evaluation•mating selection•reproduction

•evaluation•environmental

selection

generation 2

artificial intelligence - ntnuweb.ntnu.edu.tw/~tcchiang/ai/0_syllabus.pdf · 11 21 artificial...

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